Semantic Jira - Semantic Expert Finder in the Bug Tracking Tool Jira

The semantic expert recommender extension for the Jira bug tracking system semantically searches for similar tickets in Jira and recommends experts and links to existing organizational (Wiki) knowledge for each ticket. This helps to avoid redundant work and supports the search and collaboration with experts in the project management and maintenance phase based on semantically enriched tickets in Jira.Comment: published in proceedings of the 9th International Workshop on Semantic Web Enabled Software Engineering (SWESE2013), Berlin, Germany, December 2-5, 201

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

A Personalized Knowledge Recommender System For Workspace Learning

Technology Enhanced Learning (TEL) is emerging as a popular learning approach utilized by both educational institutions and business organizations. Learning Recommender Systems (RSs) can help e-learners to cope with the data overload difficulty and suggest useful items that users may wish to use. This research aims to examine the design and implementation of personalized RS that supports individual learning in the workplace. First, a hybrid knowledge recommendation technique is proposed by combing content-based method with feedback learning method to adapt to the dynamic preference of users. Second, the design and implementation of a personalized knowledge recommender system using proposed technique in a case company is presented. Quantitative and qualitative data are collected to validate the system and evaluate its performance and impact. The preliminary results show that involving enterprise experts and target users in the system design phase can improve the system transparency and users’ trust in the system. It is also found that users’ learning attitude can be positively influenced by the system experience. This research provides important implications on employing intelligent recommender system to support workplace learning

Facilitating Creativity in Collaborative Work with Computational Intelligence Software

The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking-from the combined knowledge and abilities of people with diverse perspectives-contrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present computational intelligence software tools for that aim and an architecture for creating software mashups based on the concept of affinity space. The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools ranging from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015

prototypical implementations ; working packages in project phase II

In this technical report, we present the concepts and first prototypical imple- mentations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge. The concepts and prototypes are based on the state of art analysis and identified requirements in the CSW report IV

requirements and use cases

In this report, we introduce our initial vision of the Corporate Semantic Web as the next step in the broad field of Semantic Web research. We identify requirements of the corporate environment and gaps between current approaches to tackle problems facing ontology engineering, semantic collaboration, and semantic search. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. We propose an initial layout for an integrative architecture of a Corporate Semantic Web provided by these three core pillars